System and method for trigger-based scanning of cyber-physical assets

ABSTRACT

A system and method for trigger-based scanning of cyber-physical assets, including a distributed operating system, parameter evaluation engine, at least one cyber-physical asset, at least one crypt-ledger, a network, and a scanner that detects trigger conditions and events and performs scans of cyber-physical assets based on the trigger and any relevant stored scan rules before storing scan results as time-series data.

CROSS-REFERENCE TO RELATED APPLICATIONS

Application No. Date Filed Title Current Herewith A SYSTEM AND METHODFOR application TRIGGER-BASED SCANNING OF CYBER-PHYSICAL ASSETS Is acontinuation-in-part of: 16/910,623 Jun. 24, GEOLOCATION-AWARE, CYBER-2020 ENABLED INVENTORY AND ASSET MANAGEMENT SYSTEM WITH AUTOMATED STATEPREDICTION CAPABILITY which is a continuation-in-part of: 15/930,063 May12, SYSTEM AND METHODS FOR 2020 DYNAMIC GEOSPATIALLY- REFERENCEDCYBER-PHYSICAL INFRASTRUCTURE INVENTORY AND ASSET MANAGEMENT which is acontinuation of: 15/904,006 Feb. 23, 2018 A SYSTEM AND METHODS FOR Pat.Issue Date DYNAMIC GEOSPATIALLY- 10,652,219 May 12, REFERENCEDCYBER-PHYSICAL 2020 INFRASTRUCTURE INVENTORY AND ASSET MANAGEMENT whichis a continuation-in-part of: 15/725,274 Oct. 4, 2017 APPLICATION OFADVANCED Pat. Issue Date CYBERSECURITY THREAT 10,609,079 Mar. 31,MITIGATION TO ROGUE DEVICES, 2020 PRIVILEGE ESCALATION, AND RISK-BASEDVULNERABILITY AND PATCH MANAGEMENT which is a continuation-in-part of:15/655,113 Jul. 20, 2017 ADVANCED CYBERSECURITY THREAT MITIGATION USINGBEHAVIORAL AND DEEP ANALYTICS which is a continuation-in-part of:15/616,427 Jun. 7, 2017 RAPID PREDICTIVE ANALYSIS OF VERY LARGE DATASETS USING AN ACTOR-DRIVEN DISTRIBUTED COMPUTATIONAL GRAPH which is acontinuation-in-part of: 14/925,974 Oct. 28, 2015 RAPID PREDICTIVEANALYSIS OF VERY LARGE DATA SETS USING THE DISTRIBUTED COMPUTATIONALGRAPH Current Herewith A SYSTEM AND METHOD FOR application TRIGGER-BASEDSCANNING OF CYBER-PHYSICAL ASSETS Is a continuation-in-part of:16/910,623 June 24, GEOLOCATION-AWARE, CYBER- 2020 ENABLED INVENTORY ANDASSET MANAGEMENT SYSTEM WITH AUTOMATED STATE PREDICTION CAPABILITY whichis a continuation-in-part of: 15/930,063 May 12, SYSTEM AND METHODS FOR2020 DYNAMIC GEOSPATIALLY- REFERENCED CYBER-PHYSICAL INFRASTRUCTUREINVENTORY AND ASSET MANAGEMENT which is a continuation of: 15/904,006Feb. 23, 2018 A SYSTEM AND METHODS FOR Pat. Issue Date DYNAMICGEOSPATIALLY- 10,652,219 May 12, REFERENCED CYBER-PHYSICAL 2020INFRASTRUCTURE INVENTORY AND ASSET MANAGEMENT which is acontinuation-in-part of: 15/725,274 Oct. 4, 2017 APPLICATION OF ADVANCEDPat. Issue Date CYBERSECURITY THREAT 10,609,079 Mar. 31, 2020 MITIGATIONTO ROGUE DEVICES, PRIVILEGE ESCALATION, AND RISK-BASED VULNERABILITY ANDPATCH MANAGEMENT which is a continuation-in-part of: 15/655,113 Jul. 20,2017 ADVANCED CYBERSECURITY Pat. Issue Date THREAT MITIGATION USING10,735,456 Aug. 4, 2020 BEHAVIORAL AND DEEP ANALYTICS which is also acontinuation-in-part of: 15/237,625 Aug. 15, 2016 DETECTION MITIGATIONAND Pat. Issue Date REMEDIATION OF CYBERATTACKS 10,248,910 Apr. 2, 2019EMPLOYING AN ADVANCED CYBER- DECISION PLATFORM which is acontinuation-in-part of: 15/206,195 Jul. 8, 2016 ACCURATE AND DETAILEDMODELING OF SYSTEMS WITH LARGE COMPLEX DATASETS USING A DISTRIBUTEDSIMULATION ENGINE which is a continuation-in-part of: 15/186,453 Jun.18, SYSTEM FOR AUTOMATED CAPTURE 2016 AND ANALYSIS OF BUSINESSINFORMATION FOR RELIABLE BUSINESS VENTURE OUTCOME PREDICTION which is acontinuation-in-part of: 15/166,158 May 26, SYSTEM FOR AUTOMATED CAPTURE2016 AND ANALYSIS OF BUSINESS INFORMATION FOR SECURITY AND CLIENT-FACINGINFRASTRUCTURE RELIABILITY which is a continuation-in-part of:15/141,752 Apr. 28, 2016 SYSTEM FOR FULLY INTEGRATED Pat. Issue DateCAPTURE, AND ANALYSIS OF 10,860,962 Dec. 8, 2020 BUSINESS INFORMATIONRESULTING IN PREDICTIVE DECISION MAKING AND SIMULATION which is acontinuation-in-part of: 15/091,563 Apr. 5, 2016 SYSTEM FOR CAPTURE,ANALYSIS Pat. Issue Date AND STORAGE OF TIME SERIES 10,204,147 Feb. 12,2019 DATA FROM SENSORS WITH HETEROGENEOUS REPORT INTERVAL PROFILES andis also a continuation-in-part of: 14/986,536 Dec. 31, 2015 DISTRIBUTEDSYSTEM FOR LARGE Pat. Issue Date VOLUME DEEP WEB DATA 10,210,255 Feb.19, 2019 EXTRACTION and is also a continuation-in-part of: 14/925,974Oct. 28, 2015 RAPID PREDICTIVE ANALYSIS OF VERY LARGE DATA SETS USINGTHE DISTRIBUTED COMPUTATIONAL GRAPH the entire specification of each ofwhich is incorporated herein by reference.

BACKGROUND OF THE INVENTION Field of the Art

The disclosure relates to the field of network security, morespecifically to the field of detecting and scanning for changes toassets within a network.

Discussion of the State of the Art

Currently, it is possible for corporations and individuals to trackcertain assets in certain ways, to ensure their safety and ensure validoperation. For example, it is possible to track packages shipped viamany shipping corporations, and it is possible and commonplace to havetemperature controls and monitoring in certain environments such aslibraries and wine cellars. However, changes to assets such as addition,removal, or reconfiguration, necessitate updating any related assetinformation such as inventories or network models. Generally, theseupdates must be performed manually, such as scanning incoming oroutgoing inventory items to update the inventory database, but thesemanual approaches are costly and prone to human error.

What is needed is a system and methods for trigger-based scanning ofcyber-physical assets, using a time-series data store to track the stateof connected resources, and a scanner that detects changes and scansconnected cyber-physical assets to update the network information.

SUMMARY OF THE INVENTION

Accordingly, the inventor has conceived and reduced to practice, in apreferred embodiment of the invention, a system and methods fortrigger-based scanning of network resources using a directedcomputational graph. The following non-limiting summary of the inventionis provided for clarity and should be construed consistently withembodiments described in the detailed description below.

To solve the problem of assets being unreachable by remote monitoringand smart-contract systems, a system for dynamic geospatially-referencedcyber-physical infrastructure inventory and asset management,comprising: a computing device coupled to a physical asset andcomprising a first processor, a first memory, a geolocation device, anda first plurality of programming instructions stored in the memory andoperating on the processor, wherein the first plurality of programmableinstructions, when operating on the first processor, cause the computingdevice to: periodically determine a geographical location of thephysical asset using the geolocation device; generate an encrypted assetstatus update message, the status update message comprising a deviceidentifier of the first computing device and the geographical locationof the physical asset; and transmit the encrypted asset status updatemessage via a network to the second computing device; and a port scannercomprising at least a second processor, a second memory, and a secondplurality of programming instructions stored in the second memory andoperating on the second processor, wherein the second programmableinstructions, when operating on the second processor, cause the portscanner to: receive a triggering event from the first computing device,the trigger event comprising a plurality of packets received over anetwork satisfying a preconfigured condition; retrieve a plurality ofstored scan rules from the second memory or a database; perform a scanof one or more ports of the first computing device, the scan being basedon the received trigger event and the retrieved scan rules; analyze theresults of the scan; determine whether any additional scans are neededbased on the analysis, and if so initiate the needed additional scans;and when all scans related to the received trigger event have concludedand their respective results have been analyzed, transmit an encryptedscan update message comprising the scan results and the analysis resultsto the second computing device; wherein the second computing deviceverifies the authenticity of the received encrypted asset and scanstatus update messages and modifies a cyber-physical graph based uponthe contents of the verified encrypted asset and scan status messages,is disclosed.

BRIEF DESCRIPTION OF THE DRAWING FIGURES

The accompanying drawings illustrate several aspects and, together withthe description, serve to explain the principles of the inventionaccording to the aspects. It will be appreciated by one skilled in theart that the particular arrangements illustrated in the drawings aremerely exemplary and are not to be considered as limiting of the scopeof the invention or the claims herein in any way.

FIG. 1 is a diagram of an exemplary architecture of a system for thecapture and storage of time series data from sensors with heterogeneousreporting profiles according to a preferred aspect of the invention.

FIG. 2 is a diagram of an exemplary architecture of a distributedoperating system according to a preferred aspect of the invention.

FIG. 3 is a diagram of an exemplary architecture of an automatedplanning service cluster and related modules according to a preferredaspect.

FIG. 4 is a system diagram illustrating connections between corecomponents of the invention for geo-locating and tracking the status ofcyber-physical assets, according to a preferred aspect.

FIG. 5 is a method diagram illustrating key steps in the communicationbetween cyber-physical assets and remote servers, according to apreferred aspect.

FIG. 6 is a method diagram illustrating key steps in a distributedoperating system interacting with data received from cyber-physicalassets in databases to verify updates in a cryptographic ledger,according to a preferred aspect.

FIG. 7 is a method diagram illustrating several steps in the use ofsmart contracts combined with cyber-physical assets, according to apreferred aspect.

FIG. 8 is a method diagram illustrating key steps in the function of aparametric evaluation engine, according to a preferred aspect.

FIG. 9 is a system diagram illustrating the use of a scanner to detectand scan for changes in cyber-physical assets, according to a preferredaspect.

FIG. 10 is a method diagram illustrating an exemplary process forperforming a triggered scan, according to a preferred aspect.

FIG. 11 is a block diagram illustrating an exemplary hardwarearchitecture of a computing device.

FIG. 12 is a block diagram illustrating an exemplary logicalarchitecture for a client device.

FIG. 13 is a block diagram showing an exemplary architecturalarrangement of clients, servers, and external services.

FIG. 14 is another block diagram illustrating an exemplary hardwarearchitecture of a computing device.

DETAILED DESCRIPTION

The inventor has conceived, and reduced to practice, a system and methodfor trigger-based scanning of cyber-physical assets.

One or more different aspects may be described in the presentapplication. Further, for one or more of the aspects described herein,numerous alternative arrangements may be described; it should beappreciated that these are presented for illustrative purposes only andare not limiting of the aspects contained herein or the claims presentedherein in any way. One or more of the arrangements may be widelyapplicable to numerous aspects, as may be readily apparent from thedisclosure. In general, arrangements are described in sufficient detailto enable those skilled in the art to practice one or more of theaspects, and it should be appreciated that other arrangements may beutilized and that structural, logical, software, electrical and otherchanges may be made without departing from the scope of the particularaspects. Particular features of one or more of the aspects describedherein may be described with reference to one or more particular aspectsor figures that form a part of the present disclosure, and in which areshown, by way of illustration, specific arrangements of one or more ofthe aspects. It should be appreciated, however, that such features arenot limited to usage in the one or more particular aspects or figureswith reference to which they are described. The present disclosure isneither a literal description of all arrangements of one or more of theaspects nor a listing of features of one or more of the aspects thatmust be present in all arrangements.

Headings of sections provided in this patent application and the titleof this patent application are for convenience only and are not to betaken as limiting the disclosure in any way.

Devices that are in communication with each other need not be incontinuous communication with each other, unless expressly specifiedotherwise. In addition, devices that are in communication with eachother may communicate directly or indirectly through one or morecommunication means or intermediaries, logical or physical.

A description of an aspect with several components in communication witheach other does not imply that all such components are required. To thecontrary, a variety of optional components may be described toillustrate a wide variety of possible aspects and in order to more fullyillustrate one or more aspects. Similarly, although process steps,method steps, algorithms or the like may be described in a sequentialorder, such processes, methods and algorithms may generally beconfigured to work in alternate orders, unless specifically stated tothe contrary. In other words, any sequence or order of steps that may bedescribed in this patent application does not, in and of itself,indicate a requirement that the steps be performed in that order. Thesteps of described processes may be performed in any order practical.Further, some steps may be performed simultaneously despite beingdescribed or implied as occurring non-simultaneously (e.g., because onestep is described after the other step). Moreover, the illustration of aprocess by its depiction in a drawing does not imply that theillustrated process is exclusive of other variations and modificationsthereto, does not imply that the illustrated process or any of its stepsare necessary to one or more of the aspects, and does not imply that theillustrated process is preferred. Also, steps are generally describedonce per aspect, but this does not mean they must occur once, or thatthey may only occur once each time a process, method, or algorithm iscarried out or executed. Some steps may be omitted in some aspects orsome occurrences, or some steps may be executed more than once in agiven aspect or occurrence.

When a single device or article is described herein, it will be readilyapparent that more than one device or article may be used in place of asingle device or article. Similarly, where more than one device orarticle is described herein, it will be readily apparent that a singledevice or article may be used in place of the more than one device orarticle.

The functionality or the features of a device may be alternativelyembodied by one or more other devices that are not explicitly describedas having such functionality or features. Thus, other aspects need notinclude the device itself.

Techniques and mechanisms described or referenced herein will sometimesbe described in singular form for clarity. However, it should beappreciated that particular aspects may include multiple iterations of atechnique or multiple instantiations of a mechanism unless notedotherwise. Process descriptions or blocks in figures should beunderstood as representing modules, segments, or portions of code whichinclude one or more executable instructions for implementing specificlogical functions or steps in the process. Alternate implementations areincluded within the scope of various aspects in which, for example,functions may be executed out of order from that shown or discussed,including substantially concurrently or in reverse order, depending onthe functionality involved, as would be understood by those havingordinary skill in the art.

Definitions

As used herein, a “swimlane” is a communication channel between a timeseries sensor data reception and apportioning device and a data storemeant to hold the apportioned data time series sensor data. A swimlaneis able to move a specific, finite amount of data between the twodevices. For example a single swimlane might reliably carry and haveincorporated into the data store, the data equivalent of 5 seconds worthof data from 10 sensors in 5 seconds, this being its capacity. Attemptsto place 5 seconds worth of data received from 6 sensors using oneswimlane would result in data loss.

As used herein, a “metaswimlane” is an as-needed logical combination oftransfer capacity of two or more real swimlanes that is transparent tothe requesting process. Sensor studies where the amount of data receivedper unit time is expected to be highly heterogeneous over time may beinitiated to use metaswimlanes. Using the example used above that asingle real swimlane can transfer and incorporate the 5 seconds worth ofdata of 10 sensors without data loss, the sudden receipt of incomingsensor data from 13 sensors during a 5 second interval would cause thesystem to create a two swimlane metaswimlane to accommodate the standard10 sensors of data in one real swimlane and the 3 sensor data overage inthe second, transparently added real swimlane, however no changes to thedata receipt logic would be needed as the data reception andapportionment device would add the additional real swimlanetransparently.

Conceptual Architecture

FIG. 9 is a system diagram illustrating the use of a scanner 910 todetect and scan for changes in cyber-physical assets, according to apreferred aspect. A distributed operating system 410 (described ingreater detail below, with reference to FIG. 4 ) is connected to anetwork 450, which may be an intranet, the internet, a local areaconnection, or any one of many other configurations of networks. Alsoconnected to this network 450 is at least one database 420, which holdsinformation including a crypto-ledger 421, an implementation of ablockchain data construct, which will be expounded upon in laterfigures. Connected to a network 450 is at least one cyber-physical asset430, 440, which may comprise a geolocation sensor or device (forexample, a GPS receiver) as well as any number of additional sensors orstored data according to a specific implementation, and may have geoJSON431, 441 data with which to record their geo-physical location. Acyber-physical asset 430, 440 may be a delivery crate with a possibleplurality of sensors and computers embedded or attached to the crate insome way, or may be an object inside a mundane crate such as a piece ofresearch equipment which may communicate with a distributed operatingsystem 410 during transit, or may be a stationary object such asresearch equipment, computer systems, and more, which are capable ofsending status updates at least consisting of geoJSON 431, 441information regarding their geophysical location over a network 450.

A scanner 910 may be connected to the network to detect, and act on, anychanges in cyber-physical assets 430, 440. Scanner 910 may also beconfigurable to perform manual scans in response to a command from anadministrator, or according to a preconfigured schedule, or based onexternal events such as (for example) inventory changes, market changes,publication of a known vulnerability such as a zero-day exploit or anewly-discovered security issue, announcement of a software update orproduct release, or any other configurable trigger event or condition.Scanner 910 may monitor cyber-physical assets 430, 440 for any changes,such as the addition or removal of a cyber-physical asset, or changes toa cyber-physical asset's current state or configuration (such as changesin a network configuration, or changes to an installed softwareversion), or any other change that may be observable over the network450. Additionally, scanner 910 may listen for trigger events comprisinga notification of a change that has occurred, such as (for example) anindication of a network configuration change such as a change in domainname service (DNS) resolution or a change in a router or gatewayaddress, or a change in a cyber-physical asset's assigned Internetprotocol (IP) address, or any other automated notification or indicationof a change that may be sent to, or observed by, scanner 910.

When a trigger event occurs or a trigger condition is met (such as thereceipt of a change notification or the receipt of a manual trigger froma system administrator, as described above), scanner 910 may retrieveany configured rules that may be stored in database 420 that may definescan behavior, and then determine a scan to perform based on the triggerand rules configuration. A scan may comprise any of a variety oftechniques in any combination, including (but not limited to) initiatinga port scan of a target host, transmitting a probe packet with aspecific data payload to provoke a response that may be analyzed,scanning for running services or known vulnerabilities on a hostmachine, testing a target for a specific capability or vulnerability(for example, such a scan may be triggered in response to theannouncement of a vulnerability, ensuring that systems are testedagainst potential security issues as they are discovered), or any othernetwork scan or test technique.

The results of a scan, including any responses or observed behaviors ofa target host, may then be analyzed to determine the capabilities orvulnerabilities of, or changes to, a cyber-physical asset. For example,a newly-added device may be scanned to determine its specificcapabilities and vulnerabilities, or an existing asset may be scanned todetermine the nature and extent of a change that occurred. These resultsmay then be stored in the database 420 for future use, such as toproduce or update a network map or cyber-physical graph of assets basedon their current state.

FIG. 10 is a method diagram illustrating an exemplary process 1000 forperforming a triggered scan, according to a preferred aspect. When atrigger event is received or a trigger condition is met 1001, a scannermay retrieve stored scan rules 1002 from a database 420. For example, ifa scheduled scan is configured, a scheduling event may prompt scanner910 to retrieve stored configuration rules for that particular scheduledscan, enabling scheduling of different scan types or levels of sangranularity on different schedules (for example, performing a cursoryscan for new devices daily, while performing a more detailed full scanof every device on a rotating weekly or monthly basis, or otherconfigurations). In another example, a trigger event may be receivedsuch as an announcement of a newly-discovered vulnerability, promptingscanner 910 to retrieve stored rules governing the behavior of suchtriggered scans (for example, retrieving details of the newvulnerability and configuring a scan to target that specific system orissue to test a system against it). A scan may then be performed 1003according to the trigger and any rules that were retrieved (and itshould be noted that a scan may be performed even when no rules arestored, enabling ad-hoc scans in response to trigger events), and thescan results may then be collected and analyzed 1004 to determine thescan outcome (such as any open ports or other vulnerabilitiesdiscovered, new hosts identified on a network, vulnerability to anannounced exploit, or other scan findings). Based on these results andany configured rules that may be relevant, the scanner may determinewhether additional scans are required 1005 and perform them as needed.For example, there may be a scan configuration rule to ensure anadditional, more-thorough scan is performed when a potentialvulnerability is discovered, or to perform a number of scans on anynewly-discovered network hosts, or other multi-scan configurations. Whenall scans have been completed (respective of the initial trigger 1001,it should be appreciated that a scanner may operate continually in acontinuously-updating real-time network awareness configuration, inwhich case there may be no clear moment when all scans are completed)the results may be stored 1006 in a multidimensional time-seriesdatabase (MDTSDB) 125 as time-series data with timestamps for variousrelevant metadata such as “when trigger was received”, “when first scanbegan”, “when first scan concluded”, “when first response from targethost was received”, or any other relevant time-dependent informationthat may be useful in future analysis or modeling of scan information.

FIG. 1 is a diagram of an exemplary architecture of a system for thecapture and storage of time series data from sensors with heterogeneousreporting profiles according to a preferred aspect of the invention. Inthis embodiment, a plurality of sensor devices 110 a-n stream data to acollection device, in this case a web server acting as a network gateway115. These sensors 110 a-n can be of several forms, some non-exhaustiveexamples being: physical sensors measuring humidity, pressure,temperature, orientation, and presence of a gas; or virtual such asprogramming measuring a level of network traffic, memory usage in acontroller, and number of times the word “refill” is used in a stream ofemail messages on a particular network segment, to name a small few ofthe many diverse forms known to the art. In the embodiment, the sensordata is passed without transformation to the data management engine 120,where it is aggregated and organized for storage in a specific type ofdata store 125 designed to handle the multidimensional time series dataresultant from sensor data. Raw sensor data can exhibit highly differentdelivery characteristics. Some sensor sets may deliver low to moderatevolumes of data continuously. It would be infeasible to attempt to storethe data in this continuous fashion to a data store as attempting toassign identifying keys and the to store real time data from multiplesensors would invariably lead to significant data loss. In thiscircumstance, the data stream management engine 120 would hold incomingdata in memory, keeping only the parameters, or “dimensions” from withinthe larger sensor stream that are pre-decided by the administrator ofthe study as important and instructions to store them transmitted fromthe administration device 112. The data stream management engine 120would then aggregate the data from multiple individual sensors andapportion that data at a predetermined interval, for example, every 10seconds, using the timestamp as the key when storing the data to amultidimensional time series data store over a single swimlane ofsufficient size. This highly ordered delivery of a foreseeable amount ofdata per unit time is particularly amenable to data capture and storagebut patterns where delivery of data from sensors occurs irregularly andthe amount of data is extremely heterogeneous are quite prevalent. Inthese situations, the data stream management engine cannot successfullyuse strictly single time interval over a single swimlane mode of datastorage. In addition to the single time interval method the inventionalso can make use of event based storage triggers where a predeterminednumber of data receipt events, as set at the administration device 112,triggers transfer of a data block consisting of the apportioned numberof events as one dimension and a number of sensor ids as the other. Inthe embodiment, the system time at commitment or a time stamp that ispart of the sensor data received is used as the key for the data blockvalue of the value-key pair. The invention can also accept a raw datastream with commitment occurring when the accumulated stream datareaches a predesigned size set at the administration device 112.

It is also likely that that during times of heavy reporting from amoderate to large array of sensors, the instantaneous load of data to becommitted will exceed what can be reliably transferred over a singleswimlane. The embodiment of the invention can, if capture parameterspre-set at the administration device 112, combine the data movementcapacity of two or more swimlanes, the combined bandwidth dubbed ametaswimlane, transparently to the committing process, to accommodatethe influx of data in need of commitment. All sensor data, regardless ofdelivery circumstances are stored in a multidimensional time series datastore 125 which is designed for very low overhead and rapid data storageand minimal maintenance needs to sap resources. The embodiment uses akey-value pair data store examples of which are Riak, Redis and BerkeleyDB for their low overhead and speed, although the invention is notspecifically tied to a single data store type to the exclusion of othersknown in the art should another data store with better response andfeature characteristics emerge. Due to factors easily surmised by thoseknowledgeable in the art, data store commitment reliability is dependenton data store data size under the conditions intrinsic to time seriessensor data analysis. The number of data records must be kept relativelylow for the herein disclosed purpose. As an example one group ofdevelopers restrict the size of their multidimensional time serieskey-value pair data store to approximately 8.64×10⁴ records, equivalentto 24 hours of 1 second interval sensor readings or 60 days of 1 minuteinterval readings. In this development system the oldest data is deletedfrom the data store and lost. This loss of data is acceptable underdevelopment conditions but in a production environment, the loss of theolder data is almost always significant and unacceptable. The inventionaccounts for this need to retain older data by stipulating that ageddata be placed in long term storage. In the embodiment, the archivalstorage is included 130. This archival storage might be locally providedby the user, might be cloud based such as that offered by Amazon WebServices or Google or could be any other available very large capacitystorage method known to those skilled in the art.

Reliably capturing and storing sensor data as well as providing forlonger term, offline, storage of the data, while important, is only anexercise without methods to repetitively retrieve and analyze mostlikely differing but specific sets of data over time. The inventionprovides for this requirement with a robust query language that bothprovides straightforward language to retrieve data sets bounded bymultiple parameters, but to then invoke several transformations on thatdata set prior to output. In the embodiment isolation of desired datasets and transformations applied to that data occurs using pre-definedquery commands issued from the administration device 112 and acted uponwithin the database by the structured query interpreter 135. Below is ahighly simplified example statement to illustrate the method by which avery small number of options that are available using the structuredquery interpreter 135 might be accessed.

SELECT [STREAMING|EVENTS] data_spec FROM [unit] timestamp TO timestampGROUPBY (sensor_id, identifier) FILTER [filter_identifier] FORMAT[sensor [AS identifier] [, sensor [AS identifier]] . . . ](TEXT|JSON|FUNNEL|KML|GEOJSON|TOPOJSON);

Here “data_spec” might be replaced by a list of individual sensors froma larger array of sensors and each sensor in the list might be given ahuman readable identifier in the format “sensor AS identifier”. “unit”allows the researcher to assign a periodicity for the sensor data suchas second (s), minute (m), hour (h). One or more transformationalfilters, which include but a not limited to: mean, median, variance,standard deviation, standard linear interpolation, or Kalman filteringand smoothing, may be applied and then data formatted in one or moreformats examples of with are text, JSON, KML, GEOJSON and TOPOJSON amongothers known to the art, depending on the intended use of the data.

FIG. 2 is a diagram of an exemplary architecture of a distributedoperating system 200 according to a preferred aspect. Client access tothe system 205 both for system control and for interaction with systemoutput such as automated predictive decision making and planning andalternate pathway simulations, occurs through the system's highlydistributed, very high bandwidth cloud interface 210 which isapplication driven through the use of the Scala/Lift developmentenvironment and web interaction operation mediated by AWS ELASTICBEANSTALK™, both used for standards compliance and ease of development.Much of the data analyzed by the system both from sources within theconfines of the client, and from cloud-based sources, also enter thesystem through the cloud interface 210, data being passed to theanalysis and transformation components of the system, the directedcomputational graph module 255, high volume web crawling module 215 andmultidimensional time series database 220. The directed computationalgraph retrieves one or more streams of data from a plurality of sources,which includes, but is in no way not limited to, a number of physicalsensors, web-based questionnaires and surveys, monitoring of electronicinfrastructure, crowd sourcing campaigns, and human input deviceinformation. Within the directed computational graph, data may be splitinto two identical streams, wherein one sub-stream may be sent for batchprocessing and storage while the other sub-stream may be reformatted fortransformation pipeline analysis. The data is then transferred togeneral transformer service 260 for linear data transformation as partof analysis or decomposable transformer service 250 for branching oriterative transformations that are part of analysis. The directedcomputational graph 255 represents all data as directed graphs where thetransformations are nodes and the result messages betweentransformations edges of the graph. These graphs which containconsiderable intermediate transformation data are stored and furtheranalyzed within graph stack module 245. High volume web crawling module215 uses multiple server hosted preprogrammed web spiders to find andretrieve data of interest from web-based sources that are not welltagged by conventional web crawling technology. Multiple dimension timeseries database module 220 receives data from a large plurality ofsensors that may be of several different types. The module is designedto accommodate irregular and high volume surges by dynamically allottingnetwork bandwidth and server processing channels to process the incomingdata. Data retrieved by the multidimensional time series database 220and the high volume web crawling module 215 may be further analyzed andtransformed into task optimized results by the directed computationalgraph 255 and associated general transformer service 250 anddecomposable transformer service 260 modules.

Results of the transformative analysis process may then be combined withfurther client directives, additional rules and practices relevant tothe analysis and situational information external to the alreadyavailable data in the automated planning service module 230 which alsoruns powerful predictive statistics functions and machine learningalgorithms to allow future trends and outcomes to be rapidly forecastbased upon the current system derived results and choosing each aplurality of possible decisions. Using all available data, the automatedplanning service module 230 may propose decisions most likely to resultis the most favorable outcome with a usably high level of certainty.Closely related to the automated planning service module in the use ofsystem derived results in conjunction with possible externally suppliedadditional information in the assistance of end user decision making,the outcome simulation module 225 coupled with the end user facingobservation and state estimation service 240 allows decision makers toinvestigate the probable outcomes of choosing one pending course ofaction over another based upon analysis of the current available data.For example, the pipelines operations department has reported a verysmall reduction in crude oil pressure in a section of pipeline in ahighly remote section of territory. Many believe the issue is entirelydue to a fouled, possibly failing flow sensor, others believe that it isa proximal upstream pump that may have foreign material stuck in it.Correction of both of these possibilities is to increase the output ofthe effected pump to hopefully clean out it or the fouled sensor. Afailing sensor will have to be replaced at the next maintenance cycle. Afew, however, feel that the pressure drop is due to a break in thepipeline, probably small at this point, but even so, crude oil isleaking and the remedy for the fouled sensor or pump option could makethe leak much worse and waste much time afterwards. The company doeshave a contractor about 8 hours away or could rent satellite time tolook but both of those are expensive for a probable sensor issue,significantly less than cleaning up an oil spill though and then withsignificant negative public exposure. These sensor issues have happenedbefore and the distributed operating system 200 has data from them,which no one really studied due to the great volume of columnar figures,so the alternative courses 225, 240 of action are run. The system, basedon all available data predicts that the fouled sensor or pump areunlikely the root cause this time due to other available data and thecontractor is dispatched. She finds a small breach in the pipeline.There will be a small cleanup and the pipeline needs to be shutdown forrepair but multiple tens of millions of dollars have been saved. This isjust one example of a great many of the possible use of the distributedoperating system, those knowledgeable in the art will easily formulatemore.

FIG. 3 is a diagram of an exemplary architecture of an automatedplanning service module and related modules 300 according to anembodiment of the invention. Seen here is a more detailed view of theautomated planning service module 230 as depicted in FIG. 2 . The modulefunctions by receiving decision or venture candidates as well asrelevant currently available related data and any campaign analysismodification commands through a client interface 305. The module mayalso be used provide transformed data or run parameters to the actionoutcome simulation module 225 to seed a simulation prior to run or totransform intermediate result data isolated from one or more actorsoperating in the action outcome simulation module 225, during asimulation run. Significant amounts of supporting information such as,but not restricted to current conditions, infrastructure, ongoingventure status, financial status, market conditions, and world eventswhich may impact the current decision or venture that have beencollected by the distributed operating system as a whole and stored insuch data stores as the multidimensional times series database 220, theanalysis capabilities of the directed computational graph module 255 andweb-based data retrieval abilities of the high volume web crawler module215 all of which may be stored in one or more data stores 320, 325 mayalso be used during simulation of alternative decision progression,which may entail such variables as, but are not limited toimplementation timing, method to end changes, order and timing ofconstituent part completion or impact of choosing another goal insteadof an action currently under analysis.

Contemplated actions may be broken up into a plurality of constituentevents that either act towards the fulfillment of the venture underanalysis or represent the absence of each event by the discrete eventsimulation module 311 which then makes each of those events availablefor information theory based statistical analysis 312, which allows thecurrent decision events to be analyzed in light of similar events underconditions of varying dis-similarity using machine learned criteriaobtained from that previous data; results of this analysis in additionto other factors may be analyzed by an uncertainty estimation module 313to further tune the level of confidence to be included with the finishedanalysis. Confidence level would be a weighted calculation of the randomvariable distribution given to each event analyzed. Prediction of theeffects of at least a portion of the events involved with a ventureunder analysis within a system as complex as anything from themicroenvironment in which the client operates to more expansive arenasas the regional economy or further, from the perspective of success ofthe client is calculated in dynamic systems extraction and inferencemodule 314, which use, among other tools algorithms based upon Shannonentropy, Hartley entropy and mutual information dependence theory.

Of great importance in any decision or new venture is the amount ofvalue that is being placed at risk by choosing one decision overanother. Often this value is monetary but it can also be competitiveplacement, operational efficiency or customer relationship based, forexample: the may be the effects of keeping an older, possibly somewhatmalfunctioning customer relationship management system one more quarterinstead of replacing it for $14 million dollars and a subscription fee.The automated planning service module has the ability predict theoutcome of such decisions per value that will be placed at risk usingprogramming based upon the Monte Carlo heuristic model 316 which allowsa single “state” estimation of value at risk. It is very difficult toanticipate the amount of computing power that will be needed to completeone or more of these decision analyses which can vary greatly inindividual needs and often are run with several alternativesconcurrently. The invention is therefore designed to run on expandableclusters 315, in a distributed, modular, and extensible approach, suchas, but not exclusively, offerings of Amazon's AWS. Similarly, theseanalysis jobs may run for many hours to completion and many clients maybe anticipating long waits for simple “what if” options which will notaffect their operations in the near term while other clients may havecome upon a pressing decision situation where they need alternatives assoon as possible. This is accommodated by the presence of a job queuethat allows analysis jobs to be implemented at one of multiple prioritylevels from low to urgent. In case of a change in more hypotheticalanalysis jobs to more pressing, job priorities can also be changedduring run without loss of progress using the priority based job queue318.

Structured plan analysis result data may be stored in either a generalpurpose automated planning engine executing Action Notation ModelingLanguage (ANML) scripts for modeling which can be used to prioritizeboth human and machine-oriented tasks to maximize reward functions overfinite time horizons 317 or through the graph-based data store 245,depending on the specifics of the analysis in complexity and time run.

The results of analyses may be sent to one of two client facingpresentation modules, the action outcome simulation module 225 or themore visual simulation capable observation and state estimation module240 depending on the needs and intended usage of the data by the client.

FIG. 4 is a system diagram illustrating connections between corecomponents of the invention for geo-locating and tracking the status ofcyber-physical assets, according to a preferred aspect. A distributedoperating system 410 operates an optimization engine 411, parametricevaluation engine 412, and uses abstract data representations 413including Markov State Models (MSM) 414 and abstract representations offinite state machines 415 to read, modify, and generally operate ondata. A distributed operating system 410 such as this is connected to anetwork 450, which may be an intranet, the internet, a local areaconnection, or any one of many other configurations of networks. Alsoconnected to this network 450 is at least one database 420, which holdsinformation including a crypto-ledger 421, an implementation of ablockchain data construct, which will be expounded upon in laterfigures. Connected to a network 450 is at least one cyber-physical asset430, 440, which may hold any number of sensors or data according to aspecific implementation, and have geoJSON 431, 441 data with which torecord their geo-physical location. A cyber-physical asset 430, 440 maybe a delivery crate with a possible plurality of sensors and computersembedded or attached to the crate in some way, or may be an objectinside a mundane crate such as a piece of research equipment which maycommunicate with a distributed operating system 410 during transit, ormay be a stationary object such as research equipment, computer systems,and more, which are capable of sending status updates at leastconsisting of geoJSON 431, 441 information regarding their geophysicallocation over a network 450. A distributed operating system may use aMarkov State Model (MSM) 414 as a tool for data representation of thestates of cyber-physical assets which send status updates in this way,and may or may not reduce a MSM to a finite state machine representation415 with or without stochastic elements, according to a preferredaspect. These data representations 413 are useful for visualizing andanalyzing current, previous, and possible future states of assets 430,440 connected to an operating system 410 over a network 450.

FIG. 5 is a method diagram illustrating key steps in the communicationbetween cyber-physical assets 430, 440 and remote servers running adistributed operating system 410, according to a preferred aspect. Anyrelevant sensors or sensing equipment and software must be installed onthe asset 510 first, before relevant data can be sent to a distributedoperating system 410. Such sensors may include a variety ofimplementations, including temperature sensors, GPS tracking software,accelerometers, or any other sensors and accompanying hardware andsoftware as needed or desired by the user upon implementation of thissystem. The cyber-physical asset 430, 440 will maintain, as part oftheir software involvement in the system, a private key, and therequisite software for a crypto-ledger 421 implementation 520 usingblockchain technology. Blockchain technology is essentially a method forsecure message sending between network connected devices, often used forthe purposes of transaction ledgers and smart contracts, usingasymmetric encryption. The cyber-physical asset will be in communicationwith a distributed operating system 410 either continuously or at setintervals 530, depending on individual implementations, according to apreferred aspect. During these communications, the asset will, using theasymmetric encryption in blockchain crypto-ledgers, send status updatesbased on any sensors installed on the asset 530. A distributed operatingsystem that receives these updates will then verify them with previousstatus updates in databases 540 to ensure that the updates received arelegitimate, and not forged or from a dubious source. If the public key,or signature, or contents of the encrypted message are not able to beverified properly, the ledger held in at least one database is notupdated 560. If they are properly verified and indicate they are fromthe real asset and indicate a legitimate status update, any databaseswhich hold a copy of the crypto-ledger 421 are updated with the newstatus of the asset 550. It will be apparent to one skilled in the artthat additional uses for an update verification process may be thatpartial updates (for example, with certain pieces of data not sent tothe server in the status update) may be used, and with this partialobservability, missing data between status updates may be inferred usingmachine learning techniques. It is possible to implement a rules enginefor this purpose, to determine what rules to apply for inference ofmissing data, depending on the implementation of the system.

FIG. 6 is a method diagram illustrating key steps in a distributedoperating system 410 interacting with data received from cyber-physicalassets 430, 440 in databases 420 to verify updates in a cryptographicledger 421, according to a preferred aspect. Any asset must generate apublic and private key 610 in accordance with the specifications ofasymmetric encryption, which are known technologies in the art. An assetmust prepare an update 620, which may mean formatting data received fromany installed sensors, performing any relevant calculations ormodifications to raw data, and preparing any network devices for sendingthe data across a network 450. The cyber-physical asset 430, 440 mustsign any update with its private key 630, which encrypts the update in away that only the private or public keys can be used to decrypt. Theasset, when connected to a network 450, may send the prepared andencrypted update to any “nodes” or computer systems running adistributed operating system 410, to be verified before being added ontothe ledger 421, 640. Any nodes running a distributed operating system410 will attempt to verify the asset status update 650, before thenverifying with the ledger held in at least one database 420 and anyother relevant nodes or computer systems with such a distributedoperating system 410 that the asset update is legitimate, valid, andshall be added to the ledger of status updates from the asset 660. It ispossible to implement this system and method in an ongoingidentification and authentication service, for continuous updates,rather than discrete authentication and verification for discreteupdates.

FIG. 7 is a method diagram illustrating several steps in the use ofsmart contracts combined with cyber-physical assets, according to apreferred aspect. Such smart contracts are possible as a result ofimplementing blockchain technology to not only keep track of and verifyentries in crypto-ledgers 421, but to store and execute distributedprograms, for the purposes of self-enforcing contracts, known as smartcontracts. In this implementation, a smart contract is implemented witha domain-specific-language (DSL) which may be provided by a vendor ofthe system or specified by a user of the system 710. A DSL may bethought of as a custom programming language, and may, depending on theimplementation, also be an otherwise unmodified implementation of aprogramming language, according to a preferred aspect. Conditions forsmart contracts in this system may be based on the past, present, orfuture status of cyber-physical assets monitored by the system 720. Uponcompletion of whatever conditions are programmed into a smart contract,the contract program executes, which may perform any number of tasksthat may be programmed into a computer, including withdrawal of funds,depositing of funds, messages sent across a network 450, or othersimilar results of an executed program 730, according to a preferredaspect. These parametrically-triggered remuneration contracts may beversatile and diverse in their implementation according to the needs ofthe consumer.

FIG. 8 is a method diagram illustrating key steps in the function of aparametric evaluation engine 412, according to a preferred aspect. Aparametric evaluation engine 412 may query at least one database 420 fora ledger 421 containing previous or current status updates of at leastone cyber-physical asset 430, 440, 810. This query may be performedacross a network 450 from a distributed operating system 410 run on acomputer system and may take the form of any database query format,including NOSQL™ databases such as MONGODB™, or SQL™ databases includingMICROSOFT SQL SERVER™ and MYSQL™ databases, depending on the desireddatabase implementation in the system, according to a preferred aspect.Asset status histories may be returned to a parametric evaluation engine412, which may be listed to a user of the engine, in a basic userinterface which allows the listing and searching of such asset statusupdate histories 820. Asset statuses may be viewed over time as ahistory rather than listed separately, if desired, for the purpose ofnoting and examining trends in an asset's status 830, according to anaspect.

Hardware Architecture

Generally, the techniques disclosed herein may be implemented onhardware or a combination of software and hardware. For example, theymay be implemented in an operating system kernel, in a separate userprocess, in a library package bound into network applications, on aspecially constructed machine, on an application-specific integratedcircuit (“ASIC”), or on a network interface card.

Software/hardware hybrid implementations of at least some of the aspectsdisclosed herein may be implemented on a programmable network-residentmachine (which should be understood to include intermittently connectednetwork-aware machines) selectively activated or reconfigured by acomputer program stored in memory. Such network devices may havemultiple network interfaces that may be configured or designed toutilize different types of network communication protocols. A generalarchitecture for some of these machines may be described herein in orderto illustrate one or more exemplary means by which a given unit offunctionality may be implemented. According to specific aspects, atleast some of the features or functionalities of the various aspectsdisclosed herein may be implemented on one or more general-purposecomputers associated with one or more networks, such as for example anend-user computer system, a client computer, a network server or otherserver system, a mobile computing device (e.g., tablet computing device,mobile phone, smartphone, laptop, or other appropriate computingdevice), a consumer electronic device, a music player, or any othersuitable electronic device, router, switch, or other suitable device, orany combination thereof. In at least some aspects, at least some of thefeatures or functionalities of the various aspects disclosed herein maybe implemented in one or more virtualized computing environments (e.g.,network computing clouds, virtual machines hosted on one or morephysical computing machines, or other appropriate virtual environments).

Referring now to FIG. 11 , there is shown a block diagram depicting anexemplary computing device 10 suitable for implementing at least aportion of the features or functionalities disclosed herein. Computingdevice 10 may be, for example, any one of the computing machines listedin the previous paragraph, or indeed any other electronic device capableof executing software- or hardware-based instructions according to oneor more programs stored in memory. Computing device 10 may be configuredto communicate with a plurality of other computing devices, such asclients or servers, over communications networks such as a wide areanetwork a metropolitan area network, a local area network, a wirelessnetwork, the Internet, or any other network, using known protocols forsuch communication, whether wireless or wired.

In one embodiment, computing device 10 includes one or more centralprocessing units (CPU) 12, one or more interfaces 15, and one or morebusses 14 (such as a peripheral component interconnect (PCI) bus). Whenacting under the control of appropriate software or firmware, CPU 12 maybe responsible for implementing specific functions associated with thefunctions of a specifically configured computing device or machine. Forexample, in at least one embodiment, a computing device 10 may beconfigured or designed to function as a server system utilizing CPU 12,local memory 11 and/or remote memory 16, and interface(s) 15. In atleast one embodiment, CPU 12 may be caused to perform one or more of thedifferent types of functions and/or operations under the control ofsoftware modules or components, which for example, may include anoperating system and any appropriate applications software, drivers, andthe like.

CPU 12 may include one or more processors 13 such as, for example, aprocessor from one of the Intel, ARM, Qualcomm, and AMD families ofmicroprocessors. In some embodiments, processors 13 may includespecially designed hardware such as application-specific integratedcircuits (ASICs), electrically erasable programmable read-only memories(EEPROMs), field-programmable gate arrays (FPGAs), and so forth, forcontrolling operations of computing device 10. In a specific embodiment,a local memory 11 (such as non-volatile random access memory (RAM)and/or read-only memory (ROM), including for example one or more levelsof cached memory) may also form part of CPU 12. However, there are manydifferent ways in which memory may be coupled to system 10. Memory 11may be used for a variety of purposes such as, for example, cachingand/or storing data, programming instructions, and the like. It shouldbe further appreciated that CPU 12 may be one of a variety ofsystem-on-a-chip (SOC) type hardware that may include additionalhardware such as memory or graphics processing chips, such as a QUALCOMMSNAPDRAGON™ or SAMSUNG EXYNOS™ CPU as are becoming increasingly commonin the art, such as for use in mobile devices or integrated devices.

As used herein, the term “processor” is not limited merely to thoseintegrated circuits referred to in the art as a processor, a mobileprocessor, or a microprocessor, but broadly refers to a microcontroller,a microcomputer, a programmable logic controller, anapplication-specific integrated circuit, and any other programmablecircuit.

In one embodiment, interfaces 15 are provided as network interface cards(NICs). Generally, NICs control the sending and receiving of datapackets over a computer network; other types of interfaces 15 may forexample support other peripherals used with computing device 10. Amongthe interfaces that may be provided are Ethernet interfaces, frame relayinterfaces, cable interfaces, DSL interfaces, token ring interfaces,graphics interfaces, and the like. In addition, various types ofinterfaces may be provided such as, for example, universal serial bus(USB), Serial, Ethernet, FIREWIRE™, THUNDERBOLT™, PCI, parallel, radiofrequency (RF), BLUETOOTH™, near-field communications (e.g., usingnear-field magnetics), 802.11 (WiFi), frame relay, TCP/IP, ISDN, fastEthernet interfaces, Gigabit Ethernet interfaces, Serial ATA (SATA) orexternal SATA (ESATA) interfaces, high-definition multimedia interface(HDMI), digital visual interface (DVI), analog or digital audiointerfaces, asynchronous transfer mode (ATM) interfaces, high-speedserial interface (HSSI) interfaces, Point of Sale (POS) interfaces,fiber data distributed interfaces (FDDIs), and the like. Generally, suchinterfaces 15 may include physical ports appropriate for communicationwith appropriate media. In some cases, they may also include anindependent processor (such as a dedicated audio or video processor, asis common in the art for high-fidelity A/V hardware interfaces) and, insome instances, volatile and/or non-volatile memory (e.g., RAM).

Although the system shown in FIG. 11 illustrates one specificarchitecture for a computing device 10 for implementing one or more ofthe inventions described herein, it is by no means the only devicearchitecture on which at least a portion of the features and techniquesdescribed herein may be implemented. For example, architectures havingone or any number of processors 13 may be used, and such processors 13may be present in a single device or distributed among any number ofdevices. In one embodiment, a single processor 13 handles communicationsas well as routing computations, while in other embodiments a separatededicated communications processor may be provided. In variousembodiments, different types of features or functionalities may beimplemented in a system according to the invention that includes aclient device (such as a tablet device or smartphone running clientsoftware) and server systems (such as a server system described in moredetail below).

Regardless of network device configuration, the system of the presentinvention may employ one or more memories or memory modules (such as,for example, remote memory block 16 and local memory 11) configured tostore data, program instructions for the general-purpose networkoperations, or other information relating to the functionality of theembodiments described herein (or any combinations of the above). Programinstructions may control execution of or comprise an operating systemand/or one or more applications, for example. Memory 16 or memories 11,16 may also be configured to store data structures, configuration data,encryption data, historical system operations information, or any otherspecific or generic non-program information described herein.

Because such information and program instructions may be employed toimplement one or more systems or methods described herein, at least somenetwork device embodiments may include nontransitory machine-readablestorage media, which, for example, may be configured or designed tostore program instructions, state information, and the like forperforming various operations described herein. Examples of suchnontransitory machine-readable storage media include, but are notlimited to, magnetic media such as hard disks, floppy disks, andmagnetic tape; optical media such as CD-ROM disks; magneto-optical mediasuch as optical disks, and hardware devices that are speciallyconfigured to store and perform program instructions, such as read-onlymemory devices (ROM), flash memory (as is common in mobile devices andintegrated systems), solid state drives (SSD) and “hybrid SSD” storagedrives that may combine physical components of solid state and hard diskdrives in a single hardware device (as are becoming increasingly commonin the art with regard to personal computers), memristor memory, randomaccess memory (RAM), and the like. It should be appreciated that suchstorage means may be integral and non-removable (such as RAM hardwaremodules that may be soldered onto a motherboard or otherwise integratedinto an electronic device), or they may be removable such as swappableflash memory modules (such as “thumb drives” or other removable mediadesigned for rapidly exchanging physical storage devices),“hot-swappable” hard disk drives or solid state drives, removableoptical storage discs, or other such removable media, and that suchintegral and removable storage media may be utilized interchangeably.Examples of program instructions include both object code, such as maybe produced by a compiler, machine code, such as may be produced by anassembler or a linker, byte code, such as may be generated by forexample a JAVA™ compiler and may be executed using a Java virtualmachine or equivalent, or files containing higher level code that may beexecuted by the computer using an interpreter (for example, scriptswritten in Python, Perl, Ruby, Groovy, or any other scripting language).

In some embodiments, systems according to the present invention may beimplemented on a standalone computing system. Referring now to FIG. 12 ,there is shown a block diagram depicting a typical exemplaryarchitecture of one or more embodiments or components thereof on astandalone computing system. Computing device 20 includes processors 21that may run software that carry out one or more functions orapplications of embodiments of the invention, such as for example aclient application 24. Processors 21 may carry out computinginstructions under control of an operating system 22 such as, forexample, a version of MICROSOFT WINDOWS™ operating system, APPLE OSX™ oriOS™ operating systems, some variety of the Linux operating system,ANDROID™ operating system, or the like. In many cases, one or moreshared services 23 may be operable in system 20, and may be useful forproviding common services to client applications 24. Services 23 may forexample be WINDOWS™ services, user-space common services in a Linuxenvironment, or any other type of common service architecture used withoperating system 21. Input devices 28 may be of any type suitable forreceiving user input, including for example a keyboard, touchscreen,microphone (for example, for voice input), mouse, touchpad, trackball,or any combination thereof. Output devices 27 may be of any typesuitable for providing output to one or more users, whether remote orlocal to system 20, and may include for example one or more screens forvisual output, speakers, printers, or any combination thereof. Memory 25may be random-access memory having any structure and architecture knownin the art, for use by processors 21, for example to run software.Storage devices 26 may be any magnetic, optical, mechanical, memristor,or electrical storage device for storage of data in digital form (suchas those described above, referring to FIG. 11 ). Examples of storagedevices 26 include flash memory, magnetic hard drive, CD-ROM, and/or thelike.

In some embodiments, systems of the present invention may be implementedon a distributed computing network, such as one having any number ofclients and/or servers. Referring now to FIG. 13 , there is shown ablock diagram depicting an exemplary architecture 30 for implementing atleast a portion of a system according to an embodiment of the inventionon a distributed computing network. According to the embodiment, anynumber of clients 33 may be provided. Each client 33 may run softwarefor implementing client-side portions of the present invention; clientsmay comprise a system 20 such as that illustrated in FIG. 12 . Inaddition, any number of servers 32 may be provided for handling requestsreceived from one or more clients 33. Clients 33 and servers 32 maycommunicate with one another via one or more electronic networks 31,which may be in various embodiments any of the Internet, a wide areanetwork, a mobile telephony network (such as CDMA or GSM cellularnetworks), a wireless network (such as WiFi, WiMAX, LTE, and so forth),or a local area network (or indeed any network topology known in theart; the invention does not prefer any one network topology over anyother). Networks 31 may be implemented using any known networkprotocols, including for example wired and/or wireless protocols.

In addition, in some embodiments, servers 32 may call external services37 when needed to obtain additional information, or to refer toadditional data concerning a particular call. Communications withexternal services 37 may take place, for example, via one or morenetworks 31. In various embodiments, external services 37 may compriseweb-enabled services or functionality related to or installed on thehardware device itself. For example, in an embodiment where clientapplications 24 are implemented on a smartphone or other electronicdevice, client applications 24 may obtain information stored in a serversystem 32 in the cloud or on an external service 37 deployed on one ormore of a particular enterprise's or user's premises.

In some embodiments of the invention, clients 33 or servers 32 (or both)may make use of one or more specialized services or appliances that maybe deployed locally or remotely across one or more networks 31. Forexample, one or more databases 34 may be used or referred to by one ormore embodiments of the invention. It should be understood by one havingordinary skill in the art that databases 34 may be arranged in a widevariety of architectures and using a wide variety of data access andmanipulation means. For example, in various embodiments one or moredatabases 34 may comprise a relational database system using astructured query language (SQL), while others may comprise analternative data storage technology such as those referred to in the artas “NoSQL” (for example, HADOOP CASSANDRA™, GOOGLE BIGTABLE™, and soforth). In some embodiments, variant database architectures such ascolumn-oriented databases, in-memory databases, clustered databases,distributed databases, or even flat file data repositories may be usedaccording to the invention. It will be appreciated by one havingordinary skill in the art that any combination of known or futuredatabase technologies may be used as appropriate, unless a specificdatabase technology or a specific arrangement of components is specifiedfor a particular embodiment herein. Moreover, it should be appreciatedthat the term “database” as used herein may refer to a physical databasemachine, a cluster of machines acting as a single database system, or alogical database within an overall database management system. Unless aspecific meaning is specified for a given use of the term “database”, itshould be construed to mean any of these senses of the word, all ofwhich are understood as a plain meaning of the term “database” by thosehaving ordinary skill in the art.

Similarly, most embodiments of the invention may make use of one or moresecurity systems 36 and configuration systems 35. Security andconfiguration management are common information technology (IT) and webfunctions, and some amount of each are generally associated with any ITor web systems. It should be understood by one having ordinary skill inthe art that any configuration or security subsystems known in the artnow or in the future may be used in conjunction with embodiments of theinvention without limitation, unless a specific security 36 orconfiguration system 35 or approach is specifically required by thedescription of any specific embodiment.

FIG. 14 shows an exemplary overview of a computer system 40 as may beused in any of the various locations throughout the system. It isexemplary of any computer that may execute code to process data. Variousmodifications and changes may be made to computer system 40 withoutdeparting from the broader scope of the system and method disclosedherein. Central processor unit (CPU) 41 is connected to bus 42, to whichbus is also connected memory 43, nonvolatile memory 44, display 47,input/output (I/O) unit 48, and network interface card (NIC) 53. I/Ounit 48 may, typically, be connected to keyboard 49, pointing device 50,hard disk 52, and real-time clock 51. NIC 53 connects to network 54,which may be the Internet or a local network, which local network may ormay not have connections to the Internet. Also shown as part of system40 is power supply unit 45 connected, in this example, to a mainalternating current (AC) supply 46. Not shown are batteries that couldbe present, and many other devices and modifications that are well knownbut are not applicable to the specific novel functions of the currentsystem and method disclosed herein. It should be appreciated that someor all components illustrated may be combined, such as in variousintegrated applications, for example Qualcomm or Samsungsystem-on-a-chip (SOC) devices, or whenever it may be appropriate tocombine multiple capabilities or functions into a single hardware device(for instance, in mobile devices such as smartphones, video gameconsoles, in-vehicle computer systems such as navigation or multimediasystems in automobiles, or other integrated hardware devices).

In various embodiments, functionality for implementing systems ormethods of the present invention may be distributed among any number ofclient and/or server components. For example, various software modulesmay be implemented for performing various functions in connection withthe present invention, and such modules may be variously implemented torun on server and/or client components.

The skilled person will be aware of a range of possible modifications ofthe various embodiments described above. Accordingly, the presentinvention is defined by the claims and their equivalents.

What is claimed is:
 1. A system for dynamic geospatially-referencedcyber-physical infrastructure inventory and asset management,comprising: a first computing device coupled to a physical asset andcomprising a first processor, a first memory, a geolocation device, anda first plurality of programming instructions stored in the memory andoperating on the processor, wherein the first plurality of programmableinstructions, when operating on the first processor, cause the firstcomputing device to periodically perform the following actions:determine a geographical location of the physical asset using thegeolocation device; generate an encrypted asset status update message,the encrypted asset status update message comprising a device identifierof the first computing device and the geographical location of thephysical asset; and transmit the encrypted asset status update messagevia a network to a second computing device; and the second computingdevice comprising a second processor, a second memory, and a secondplurality of programming instructions stored in the second memory andoperating on the second processor, wherein the second programmableinstructions, when operating on the second processor, cause the secondcomputing device to: receive a triggering event from the first computingdevice, the trigger event comprising a plurality of packets receivedover a network satisfying a preconfigured condition; attach time-seriesmetadata to the triggering event comprising a time at which thetriggering event occurred; retrieve a plurality of stored scan rulesassociated with the triggering event from the second memory or adatabase; perform an initial scan of one or more ports of the firstcomputing device using the plurality of scan rules; produce an initialscan result comprising a first list of network vulnerabilities; attachinitial time-series metadata to the initial scan result comprising atime at which the initial scan was initiated; analyze the first list ofnetwork vulnerabilities to determine whether an additional scan isneeded; when the additional scan is needed, perform the additional scanand produce an additional scan result comprising a second list ofnetwork vulnerabilities; attach additional time-series metadata to theadditional scan result comprising a time at which the additional scanwas initiated; and generate and encrypt a scan report message comprisingthe initial scan result and its attached time-series metadata, and theadditional scan result and its attached time-series metadata; transmitthe encrypted scan report message to the second computing device;wherein: the second computing device verifies the authenticity of theencrypted scan report message and modifies a cyber-physical graph toinclude the first list of network vulnerabilities and the initialtime-series data, and the second list of network vulnerabilities and theadditional time-series metadata, based upon the contents of the verifiedencrypted scan report message; and the cyber-physical graph is stored ina multidimensional time-series database, used to receive dataasynchronously from multiple sources over a period of time, andestablishing graph-series data structures with the received data.
 2. Thesystem of claim 1, wherein the attached time-series metadata comprisesthe time at which each scan was completed.